We select three competitive spoken command classification models of “pure DNN”, “QCNN-DNN”, and “kernel metric learning” as baselines. Following the setup in [1]}, we first select an attention recurrent neural network [2]}, as our “pure DNN” baseline. We next select its extended hybrid quantum-classical version (denoted as “QCNN-DNN“) proposed in [1]}, where a quantum convolution layer has been used to replace some of the neural convolution layers of a pure DNN model with a boosted performance. We use the best setup of \(2\times 2\)  convolution filter with 4 qubits in [1]} (increasing the qubits numbers up to 9 does not improve performances from our previous study [1]}). Finally, for the "kernel metric learning" baseline, we use the best setup for speech command classification proposed in [6]} with a backbone of multi-class radial basis function (RBF) based “kernel SVM” [7]} using a typical loss  [8]} to optimize its latent embedding as a distance metric.
